Search Results (14,680)

Obesity is an adiposity-based low-grade chronic inflammatory disease characterized by excessive fat accumulation and/or dysregulated adipose function, affecting health, quality of life, and mortality. Despite the implementation of public health strategies and individualized therapeutic interventions, obesity currently affects more than one billion people worldwide. Although the distinction between preclinical obesity (PCO) and clinical obesity (CO) has recently been conceptually established, practical tools capable of translating this distinction into clinical diagnosis are still lacking. In this study, we proposed a novel diagnostic framework in which the SF-36 Role-Physical (RP) domain functions as a screening tool to identify physical limitations associated with CO. The framework integrates anthropometric criteria (body mass index and waist circumference), organ dysfunction (presence of comorbidities), and objectively defined physical limitations based on RP domain cutoff values. To validate the framework, patients’ inflammatory profiles were evaluated. Our results demonstrated that stratification based on this functional model could identify a phenotype characterized by a distinct pro-inflammatory profile. CO patients exhibited significantly increased IL-6 and IL-17A levels compared with the PCO and overweight groups. Taken together, these findings suggest that this approach may serve as a simple, low-cost, and clinically applicable strategy to support differentiation between preclinical and clinical obesity. However, larger longitudinal and multicenter studies are needed to validate these findings. Full article

Object detection in complex environments remains challenging due to illumination variations, background clutter, and the presence of small objects. Multimodal detection methods based on RGB and infrared (IR) data have shown promising potential by leveraging complementary information across modalities. However, existing approaches still suffer from cross-modal feature misalignment, loss of fine-grained details, and insufficient semantic interaction. In this work, we introduce a novel dual-stream framework called DMNet, specifically tailored for visible and IR multimodal object detection. The architecture integrates four core components designed to tackle these challenges: surface detail fusion (SDF) for shallow feature alignment, wavelet feature extraction (WFE) for frequency-domain enhancement, context-guided enhancement (CGE) for semantic refinement, and adaptive spatial fusion (ASF) for multi-scale feature aggregation. We conduct extensive evaluations on three benchmark datasets, including M3FD, LLVIP, and VEDAI, demonstrating that DMNet achieves superior detection performance compared with existing methods. Experimental results confirm that DMNet outperforms existing approaches, achieving an mAP@0.5 of 78.4% on M3FD, 94.4% on LLVIP, and 59.0% on VEDAI. Notably, the model maintains a relatively compact parameter scale (5.72 million parameters) while achieving superior detection performance, making it suitable for practical deployment. These findings highlight DMNet as an effective and efficient solution for multimodal object detection under challenging conditions, especially in low-light and small-object scenarios. Full article

We propose a novel targetless extrinsic calibration method for wide-baseline and wide-angle fisheye cameras, which are mounted on a driving vehicle for surround view monitoring. Sequences of image frames from three fisheye cameras are obtained, and the object instance and depth around the vehicle are used for calibration. Thus, the proposed method can be applied to online vehicle camera calibration. Fisheye images are first transformed into the cylindrical coordinate system by considering the panoramic formation of the cameras. Then, the state-of-the-art object detection and monocular depth estimation models are applied to the cylindrical images. Vehicle instances matched across different views are reconstructed into 3D point clouds, and their depths are scaled by employing the pose geometry of the front camera. The per-point depths and global scale are then jointly optimized to achieve accurate cross-view alignment and extrinsic calibration. Experiments on both real-world and synthetic video datasets show that the proposed method achieves higher accuracy than COLMAP and DUSt3R under challenging conditions such as wide baselines and low frame rates, without requiring an artificial calibration target. Full article

Background/Objectives: As important post-transcriptional and epigenetic regulators of gene expression, miRNAs play a pivotal role in modulating host–virus interactions. While prior reviews have addressed either direct miRNA–HIV genome interactions or miRNA-mediated immune modulation in isolation, the integrated dual functionality of these molecules has not been systematically characterized. This review aimed to comprehensively explore how miRNAs that target the HIV-1 genome simultaneously modulate key innate and adaptive host immune signaling pathways. The conceptual novelty of this study is determined not by the identification of previously unknown miRNA-target gene pairs, but by the systemic integration of two regulatory levels (direct inhibition of the viral genome and modulation of the host cell immune signaling pathways) within a unified analytical framework. Such an integrated approach reveals a proviral regulatory network that remains non-obvious when each of these levels is examined separately. Methods: A narrative review was conducted using PubMed, Scopus, Web of Science, and Google Scholar (all years through 2025). In Stage 1, publications reporting experimentally confirmed interactions between host miRNAs and the HIV-1 genome were identified, yielding a curated set of 15 miRNAs. In Stage 2, target genes for each miRNA were retrieved from miRTarBase, TarBase (experimentally validated) and TargetScan 8.0 (in silico predicted). In Stage 3, target genes were manually mapped to key immune signaling pathways (TLR, NF-κB, JAK-STAT). In Stage 4, targeted literature searches were performed for each miRNA–target gene pair to identify direct experimental evidence of interaction. All stages were performed by two independent researchers, with discrepancies resolved by a third. Results: Fifteen host miRNAs with experimentally confirmed binding to the HIV-1 genome were identified, targeting viral genes including nef, pol, vpr, gag, env, vif, and the 3′-UTR. Thirteen of these miRNAs were found to regulate components of major immune pathways. miR-92a-3p, miR-29a/b-3p, miR-150-5p, and miR-125b-5p emerged as the most pleiotropic regulators, simultaneously suppressing TLR signaling (TLR3, TLR7, TLR8, MyD88, TRAF3/6, IRAK1/4), NF-κB components (REL, RELA, NFKB1), JAK-STAT effectors (STAT1–3, STAT5A/B, JAK2), and negative regulators of cytokine signaling (SOCS and PIAS family proteins). miR-133b and miR-196b-5p were found to selectively regulate SOCS/PIAS proteins without involvement in other analyzed pathways, suggesting potential for selective therapeutic targeting. Conclusions: The analyzed miRNAs exhibit functional dualism, acting as direct post-transcriptional suppressors of the HIV-1 genome while simultaneously functioning as epigenetic modulators of host immune signaling. These two modes of action are not independent but together form a conceptual framework of a self-reinforcing proviral regulatory network that, based on the synthesis of published evidence, is proposed to promote viral latency and immune evasion. The identified miRNAs represent promising, albeit complex, targets for novel therapeutic strategies aimed at eliminating latent HIV reservoirs. Full article

The convergence of Software-Defined Networking (SDN) and Blockchain (BC) creates a symbiotic relationship in which SDN’s programmable global visibility complements BC’s decentralized, immutable trust model to address critical cybersecurity vulnerabilities and cyber attacks. Addressing the fragmentation in the current literature, this study rigorously investigates BC and SDN (B-SDN) integration with the primary objectives of: (1) differentiating impacts across varied sectors, including the Internet of Things (IoT), Smart Grids, and Vehicular Ad Hoc Networks (VANETs) and more; (2) analyzing critical performance metrics such as energy efficiency and scalability; (3) classifying mitigation, detection, and prevention schemes for specific threats; (4) examining novel Artificial Intelligence (AI) methods; and (5) identifying open challenges and future research directions. Methodologically, this study conducts a survey of state-of-the-art B-SDN studies to investigate six key areas: Industry-specific applications, security mechanisms, defense strategies, defenses against specific attacks, AI integration, and implementation performance. The findings demonstrate that B-SDN integration shows strong potential in simulated and prototype environments to mitigate specific high-impact threats, such as Distributed Denial of Service (DDoS), Man-in-the-Middle (MiTM), and spoofing, across various domains, including IoT, 5G/6G, VANETS, and Smart Grid. Despite the benefits and advantages promised by B-SDN, several limitations continue to exist, including the latency–security trade-off inherent to consensus protocols and scalability constraints in large-scale deployments. Finally, open research challenges persist in AI-driven automation, particularly in Federated Learning (FL) and in the development of standardized interoperability protocols required to enable the transition from conceptual models to operational systems. Full article

Developing potent adjuvants is critical for enhancing vaccine efficacy, particularly for subunit antigens. Background/Objectives: This study evaluates a novel composite adjuvant system combining liposomal QS-21 and CpG ODNs to enhance vaccine-induced immunogenicity, particularly Th1-type cellular immunity. Methods: To mitigate QS-21’s hemolytic toxicity and ensure precision delivery, a stable liposomal formulation was developed. Mice models were established using varicella-zoster virus (VZV) glycoprotein E (gE) or ovalbumin (OVA) as antigens to evaluate humoral and cellular immune responses. Results: Immunization with gE protein formulated with this novel adjuvant synergistically triggered robust immune responses, outperforming single adjuvants and the combination of QS-21/MPL. Across broad dose ranges, it induced higher Th1-type cellular immunity and comparable humoral immunity relative to AS01_B. Mechanistic studies revealed that the adjuvant significantly enhances the recruitment of dendritic cells (DCs), monocytes, and neutrophils to draining lymph nodes (dLNs) while upregulating co-stimulatory molecules CD40 and CD86 on DCs. Furthermore, the formulation triggered robust, transient increases in Th1-associated cytokines (IFN-γ, IL-12) and chemokines (CXCL9, CXCL10) across the injection site, serum, and dLNs. Conclusions: These findings indicate that the liposomal QS-21 and CpG ODNs system is a highly effective platform for promoting robust Th1-biased immunity, offering a promising adjuvant candidate and a solid experimental foundation for developing next-generation vaccines requiring potent cellular immunity. Full article

Joining dissimilar polymers such as polypropylene (PP) and high-density polyethylene (HDPE) remains a challenge in modern manufacturing due to their incompatible thermal properties and poor interfacial bonding. In this study, a novel hybrid structure was fabricated by laser welding of PP to an HDPE matrix reinforced with 3 wt% carbon nanotubes (CNTs). The CNTs were incorporated via fused filament fabrication (FFF) 3D printing to raise the melting temperature and thermal stability of HDPE, thereby minimizing the thermal mismatch with PP. A pulsed CO₂ laser was used to perform butt welding, and the influences of pulse frequency, welding speed, and laser power on the elastic modulus and tensile properties of the weld samples were thoroughly studied. A response surface design was employed to build predictive models and perform multi-objective optimization. The addition of CNTs, as evidenced by differential scanning calorimetry (DSC), elevated the crystallinity level of HDPE from 48.3% to 53.1% and the melting point from 137.8 to 140.8 °C, making its thermal properties more comparable to those of PP. Observations via scanning electron microscopy (SEM) indicated that when the optimal parameters were applied (pulse frequency: 35 Hz, welding speed: 21 mm/s, and laser power: 49 W), the joint line was defect-free, fully fused, and contained very few voids. At these settings, the model estimated an elastic modulus of 793 MPa and a tensile strength of 49.6 MPa, while confirmation experiments yielded 47.2 MPa and 764.5 MPa, respectively, with relative errors below 5%. The results demonstrate that the combination of CNT-assisted laser welding and RSM-driven optimization effectively resolves the thermal incompatibility of HDPE and PP, thereby facilitating high-quality joining of dissimilar polymers for applications in packaging and automotive fields. Full article

Background/Objectives: Course-based Undergraduate Research Experiences (CUREs) represent a form of student crowdsourcing in which individuals perform authentic discovery-based research in a class setting with interest to outside stakeholders. Here, the renowned Tiny Earth (TE) CURE is being utilized to teach microbiology and perform natural product discovery research by students in the course. Methods: In our TE CURE, students collect soil samples from their hometown and characterize bacteria that can inhibit plant and animal pathogens. This unique growing collection of isolates from across Ohio has provided opportunities to facilitate drug discovery and establish biotechnology collaborations. Results: In this study, we describe two outcomes using our environmental strain collection that initiated biotechnology collaborations and identified bacterial candidates for drug discovery. Results from one project led to a partnership with an aquaculture company. A novel biosynthetic gene cluster involved in antagonistic activity was identified, whose product inhibits Aeromonas pathogens, which cause disease in freshwater fish. The other project involves a collaboration with a global commercial cleaning and equipment company to identify lipase activity among Bacillus strains for its potential use in bioremediation. Conclusions: The unique strain collection generated by students in the CURE led to collaboration with biotechnology companies, which contributed to natural product discovery of an antimicrobial product and active enzymatic activity, all of which benefit education and scientific discovery. Full article

Background/Objectives: Early mortality after pediatric liver transplantation remains a clinical challenge, yet few studies have specifically addressed 30-day outcomes. Conventional pretransplant scores such as the age-appropriate MELD/PELD score were not designed for post-transplant risk prediction. We aimed to evaluate whether dynamic postoperative biomarker trajectories and novel composite ratios can identify high-risk patients. Methods: This single-center retrospective cohort study included 140 consecutive pediatric patients (<18 years) who underwent primary liver transplantation between 2015 and 2023. Patients were classified as deceased (≤30 days, n = 11) or survivors (>30 days, n = 129). PRISM-III, PELOD-2, and age-appropriate MELD/PELD scores were evaluated. Serial laboratory parameters were collected at pretransplant and at 0, 24, and 72 h. Delta (Δ) values and composite ratios—including lactate clearance, lactate-to-albumin ratio (LAR), INR×lactate product, platelet ratio, and fibrinogen/INR—were calculated. Penalized logistic regression (Firth method) was used for multivariate analysis. Internal validation was performed using bootstrap resampling (1000 iterations) and leave-one-out cross-validation (LOO-CV). Because two of the three components of the multivariable model (ΔINR, ΔALT) were derived from 72-h values, the model is best understood as a 72-h landmark risk model rather than as an immediate post-transplant early-warning tool. Results: The 30-day mortality rate was 7.9% (11/140), with central nervous system complications as the leading cause (36.4%). PRISM-III demonstrated excellent discrimination (AUROC 0.957; cut-off ≥ 14); the age-appropriate MELD/PELD score, a pretransplant tool not designed for post-transplant prediction, showed near-chance performance (AUROC 0.513; p = 0.576). A distinctive biomarker crossover pattern was observed: non-survivors had paradoxically lower pretransplant INR, ALT, and LAR values, but trajectories diverged sharply by 24 h. The INR×lactate product achieved an AUROC of 0.981 at 72 h. LAR at 24 h achieved 0.909, and lactate clearance at 0 → 72 h achieved 0.783. Postoperative hypernatremia emerged as a strong predictor (AUROC 0.884). In multivariate analysis, PRISM-III (OR 4.00), ΔINR (OR 3.28), and ΔALT (OR 3.46) were independent predictors (apparent AUROC 0.989). Internal validation confirmed model stability: bootstrap-corrected AUROC was 0.978; LOO-CV AUROC was 0.957 (sensitivity 90.9%, specificity 96.9%). Conclusions: Dynamic postoperative factors—rather than pretransplant disease severity—appeared more strongly associated with 30-day mortality after pediatric liver transplantation in this single-center exploratory analysis. The INR×lactate product, a novel two-variable composite, showed very high apparent discrimination (AUROC 0.981) and is proposed as a hypothesis-generating candidate marker requiring prospective external validation before any clinical use. The combined PRISM-III + ΔINR + ΔALT model (best understood as a 72-h landmark risk model, since two of its three components are defined at 72 h postoperatively) demonstrated robust internal validation performance (LOO-CV AUROC 0.957); however, given the small number of events (n = 11) and the absence of external validation, the model should be regarded as exploratory. Full article

Background/Objectives: Chemosymbiotic bivalves play a fundamental role in deep-sea cold seep and hydrothermal vent ecosystems, where essential long-chain polyunsaturated fatty acids (LC-PUFAs) are scarce. Whether these bivalves retain the capacity for endogenous PUFA synthesis remains poorly understood. Here, we investigated the PUFA biosynthetic capacity of two dominant chemosymbiotic bivalves from the Haima cold seep—the mussel Gigantidas haimaensis and the clam Archivesica marissinica. Methods: Genome and transcriptome assembly revealed three fatty acid desaturase (Fad) genes per species, which phylogenetically clustered into Δ5 (GhFads1, GhFads2; AmFads1, AmFads2) and Δ6/8 (GhFads3, AmFads3) clades, with lineage-specific duplications within the Δ5 clade. Functional assays were performed in yeast to characterize the activity of these enzymes. Results:Both Fads3 enzymes (Δ6/8 clade) convert C20:3n-6 and C20:4n-3 into C20:4n-6 and C20:5n-3, respectively, exhibiting Δ8-desaturase activity. Notably, Δ5-clade isoforms showed divergent substrate specificities: GhFads2 and AmFads1 functioned as classical Δ5-desaturases on PUFA substrates, whereas GhFads1 and AmFads2 specifically desaturated the bacterial monounsaturated fatty acid (MUFA) C18:1n-7 to produce C18:2n PUFAs. Conclusions: Together, our results reveal that cold-seep bivalves retain endogenous LC-PUFA biosynthetic capacity and have evolved duplicated Δ5-desaturases with novel regioselectivity toward bacterial MUFAs, likely representing an adaptive membrane modification for survival under extreme deep-sea conditions. Full article

Background: The growing complexity and cost of oncohematological treatments has created an urgent need for standardized methodologies capable of enabling inter-institutional comparisons of healthcare expenditure within homogeneous patient groups. Cancer-related pharmaceutical costs vary substantially depending on tumour type, disease stage, and therapeutic approach, making cross-institutional benchmarking challenging due to heterogeneity in patient populations and clinical practice patterns. Therefore, integrating cost analysis with clinically meaningful patient stratification is essential to improve resource allocation and outcome evaluation. Methods: A multicentre working group comprising four tertiary hospitals in Madrid (Spain) was established to develop and preliminarily evaluate a novel classification system for adult oncohematological patients. A standardized methodology was designed to stratify patients into homogeneous groups (PATONCO categories) based on tumor location, therapeutic objective, and clinically relevant biomarkers. A cost indicator was defined as the average cost per patient per month for each PATONCO category. Data were extracted from pharmacy dispensing systems and analyzed using descriptive and inferential statistics, including Kruskal–Wallis and post hoc Dunn tests. Results: A total of 3659 patients were included (3168 oncology; 491 hematology), distributed across 62 programmes (54 oncology; 8 hematology). The PATONCOS tool enabled the identification and validation of a cost indicator (average cost/patient/month per category), allowing inter-hospital comparison. Significant differences in costs were observed across most high-prevalence categories, reflecting variability in drug selection within homogeneous patient groups, as documented by the differential use of specific therapeutic agents across centers. The model demonstrated its capacity to detect intra-group homogeneity and inter-group variability, improving the identification of high-cost patient subgroups and supporting benchmarking across centers. Conclusions: The PATONCOS tool provides a novel, clinically oriented stratification methodology that integrates pharmacotherapy, biomarkers, and disease stage with economic evaluation. This approach enables more accurate comparisons of oncology treatment costs between institutions and may support data-driven decision-making in resource allocation. Its implementation may contribute to more sustainable healthcare systems by aligning clinical practice with economic outcomes. Full article

Background: A novel day–night facial serum regimen combining antioxidants, hydration-enhancing agents, and bioactive compounds has been developed to address skin aging. Objective: The aim is to explore changes in skin aging parameters and assess the safety and tolerability of a day-and-night facial serum combination. Methods: In this single-arm, non-randomized, prospective, open-label study, 30 participants aged 35–55 years applied a day facial serum (DFS) and a night facial serum (NFS) for 8 weeks. Objective assessments included skin texture and depression (Antera 3D^®), elasticity and firmness (Cutometer^®), hydration (Corneometer^®), TEWL (Tewameter^®), melanin index (Mexameter^®), and brightness (Colorimeter^®). Evaluations were performed at baseline, at 1 month and 2 months after treatment start, and at 1 month post-treatment. Results: Thirty participants (Fitzpatrick III–IV) completed the study. Significant improvements in skin texture (p = 0.002) and reduction in skin depression (−22.7%, p < 0.001) were observed after 2 months. Skin firmness increased significantly at 1 month (p < 0.001) and remained elevated post-treatment (p = 0.008). The melanin index decreased at 1 month (p = 0.048), while hydration declined after discontinuation (p = 0.016). TEWL showed a significant overall time effect; however, no Bonferroni-adjusted pairwise comparison versus baseline was significant. No significant changes were observed in elasticity or brightness. Mild transient burning was the most common adverse event. Other reported adverse events included acne, miliaria rubra, oiliness, and mild itching; all were non-serious and did not result in treatment discontinuation. Conclusions: The combined DFS and NFS regimen was well tolerated and was associated with favorable changes in skin texture and firmness. Transient local reactions, particularly mild burning sensation, were commonly reported but did not result in treatment discontinuation. Further controlled studies are warranted to confirm these observations. Full article

Background/Objectives: Metabolic dysfunction-associated steatotic liver disease (MASLD), formerly termed non-alcoholic fatty liver disease (NAFLD), is the most prevalent chronic liver condition globally, affecting one-quarter of the adult population. Non-invasive metabolic indices offer a pragmatic alternative to liver biopsy for population-level steatosis screening. We aimed to systematically compare the diagnostic performance of four widely used metabolic indices—TyG, LAP, FLI, and VAI—and to derive a novel composite score that demonstrably surpasses each constituent index. Methods: Retrospective cross-sectional study; 1204 consecutive adults undergoing abdominal ultrasonography at a single tertiary centre (study period: 2024–2025; data collection period: January–April 2026); steatosis graded 0–3 by certified radiologists; four metabolic indices retrieved from pre-calculated columns; novel composite score derived by binary logistic regression with 10-fold stratified cross-validation (stratified by binary outcome); AUROC with 1000-iteration bootstrap 95% CI; and pairwise comparisons by the DeLong method. Results: Of 1204 patients (mean age 42.3 ± 12.5 years; 73.3% male; 13.4% with diabetes), steatosis grades were: Grade 0, 185 (15.4%); Grade 1, 539 (44.8%); Grade 2, 311 (25.8%); and Grade 3, 169 (14.0%). For significant steatosis (Grade ≥ 2), TyG achieved AUROC 0.884 [0.863–0.904] and LAP 0.883 [0.862–0.901], while FLI showed only moderate performance. The novel composite score achieved AUROC 0.896 [0.870–0.920] for any steatosis and 0.922 [0.906–0.938] for significant steatosis, significantly outperforming all four individual indices (DeLong p = 0.004 vs. TyG; p = 0.002 vs. LAP; p < 0.001 vs. FLI; p < 0.001 vs. VAI). Youden-optimal performance: sensitivity 87.1%, specificity 86.0%, and Youden index 0.731. Conclusions: A 10-fold cross-validated logistic composite of four metabolic indices significantly outperforms each constituent index for ultrasonographic hepatic steatosis detection. The composite score may serve as a practical, non-invasive tool for steatosis risk stratification pending external validation. Full article

Cementitious materials in the fresh state are commonly regarded as viscoplastic. That is, below a given yield stress, they exhibit solid-like behavior, whereas above this threshold, they behave as fluids. In this context, the shear strength of such materials has traditionally been analyzed from a rheological standpoint, considering them as fluids and using time as the primary state variable. From a structural perspective, however, relatively few studies have treated the material as a solid. With the advent of 3D printing technology, this trend has persisted. Within this framework, the present research aims to evaluate the shear strength of a structural mortar for 3D printing in its solid-like regime, by applying the Mohr–Coulomb failure criterion. Furthermore, in a novel approach, the degree of hydration of Portland cement is proposed as a state variable to replace time, enabling a more comprehensive and objective description of the material’s mechanical evolution. Thus, addressing this gap in the state of the art, a chemo-mechanical coupling is developed. To obtain the necessary data, direct shear, uniaxial compression, and isothermal calorimetry tests are performed. The results indicate that the friction angle remains constant, at approximately 33°, and that cohesion, the parameter governing strength gain, exhibits the same linear rate of increase with hydration in both mechanical tests, indicating an intrinsic relationship within the material. Full article

In recent years, salient object detection in optical remote sensing images (ORSI-SOD) has garnered increasing research attention. However, in practical applications, issues such as blurred target edges under low-contrast and complex background interference continue to restrict the accuracy and robustness of detection. To address these problems, this paper proposes the Phase Congruency-Guided Cross-Scale Contextual Fusion Network (PCFNet). Specifically, we design a novel Phase Congruency Enhanced Module (PCE) to solve the problem of low-contrast between targets and backgrounds. It acquire phase features via Fourier decomposition and employs them to generate a weighting map to modulate the shallow features via element-wise multiplication, thereby highlighting structurally significant regions. Meanwhile, we adopt a tailored loss weighting mechanism to weight phase congruency learning for better PCE adaptation. To address complex background interference, we design a novel Dynamic Residual Fusion (DRF) Module. It leverages dynamic spatial attention to generate sample-specific kernels that perform convolution to spatially weight features and uses consecutive residual connection, thereby refining multi-scale features to accurately capture effective targets under complex background interference. Experiments on ORSSD, EORSSD, and ORSI4199 benchmarks demonstrate that PCFNet achieves nine best performances and three second-best performances across the twelve core evaluation metrics, outperforming 23 state-of-the-art methods. Notably, the $F_{\beta }$ score is 1.16% higher than HFCNet on ORSSD and 0.85% higher than MCPNet on EORSSD. Full article